The present invention relates to classification of endoscopic images, and more particularly, to classification of endoscopic images using deep learning based networks to detect endoscopic images having abnormal cell or tissue growth.
Endoscopic image analysis plays an important role in visual diagnosis of lethal medical conditions originating primarily in the gastrointestinal tract, respiratory tract, or other vital tracts of the human body. Early and precise detection of many of these conditions can increase the chances of survival of an ailing patient through appropriate clinical procedures. For example, the relative five year survival rate of colorectal cancer is about 90% when diagnosed at an early polyp stage before it has spread. Similarly, meningioma, a benign intra-cranial tumor condition occurring in approximately seven of every 100,000 people, can be treated surgically or radiologically if detected early, thereby drastically reducing the chances of its malignancy.
Currently, clinicians visually scan endoscopic images, typically captured through endoscopic probes, for abnormal cell or tissue growth in the region under observation. Such manual screening procedures can be tedious, as a single endoscopic probe typically generates a very large number of images. Furthermore, since the screening relies heavily on the dexterity of the clinician in charge, cases of missed detection are not uncommon. Accordingly, automated computer aided diagnosis (CAD) solutions are desirable that can efficiently screen out irrelevant endoscopic images and detect endoscopic images in which abnormal cell or tissue growth is present.